Analysis of AI-Agent Implementation in Industry 5.0 Production Optimization: A Systematic Literature Review

Kim Rae Dawn (Department of Computer Science, The University of York, York, YO10 5GH, United Kingdom)

Jurnal Mekintek : Jurnal Mekanikal, Energi, Industri, Dan Teknologi
Vol. 16 No. 2 (2025): October: Mechanical, Energy, Industrial and Technology

Industry 5.0 represents the next evolution of manufacturing, emphasizing the integration of advanced technologies with human-centered, sustainable, and resilient production systems. This study aims to analyze the implementation of AI agents in Industry 5.0 production optimization and evaluate their contributions to manufacturing performance. A Systematic Literature Review (SLR) approach was employed to examine relevant studies published between 2020 and 2026, sourced from Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink. The selected literature was analyzed across three dimensions The findings indicate that various AI-agent technologies, including Intelligent Agents, Multi-Agent Systems (MAS), Reinforcement Learning Agents, and Generative AI Agents, are increasingly utilized in manufacturing environments. Their implementation has led to significant improvements in production scheduling through reduced scheduling conflicts and enhanced machine utilization, predictive maintenance through improved failure prediction and reduced downtime, quality control through intelligent defect detection and automated inspection, and resource allocation through optimized utilization of labor, machinery, materials, and energy. Furthermore, AI agents contribute substantially to the core pillars of Industry 5.0 by supporting human-centric manufacturing through decision assistance and workplace safety enhancement, promoting sustainability through waste reduction and energy optimization, and increasing operational resilience through adaptive responses to disruptions and demand fluctuations. Despite challenges related to data integration, implementation costs, workforce readiness, and ethical considerations, the overall findings demonstrate that AI agents are highly effective enablers of production optimization and intelligent manufacturing. Future research should focus on autonomous factory ecosystems, human–AI collaborative agents, Digital Twin–AI Agent integration, and Explainable AI frameworks to further advance Industry 5.0 adoption and manufacturing innovation.